A community based cross sectional study on the prevalence of dyslipidemias and 10 years cardiovascular risk scores in adults in Asmara, Eritrea

Despite the contribution of dyslipidemia to the high and rising burden of arteriosclerotic cardiovascular disease (CVD) in Sub-Saharan Africa; the condition is under-diagnosed, under-treated, and under-described. The objective of this study was to explore the prevalence of dyslipidemias, estimate a 10-year cardiovascular disease risk and associated factors in adults (≥ 35 to ≤ 85 years) living in Asmara, Eritrea. This population-based cross-sectional study was conducted among individuals without overt CVDs in Asmara, Eritrea, from October 2020 to November 2020. After stratified multistage sampling, a total of 386 (144 (37%) males and 242 (63%) females, mean age ± SD, 52.17 ± 13.29 years) respondents were randomly selected. The WHO NCD STEPS instrument version 3.1 questionnaire was used to collect data. Information on socio-demographic variables was collected via interviews by trained data collectors. Measurements/or analyses including anthropometric, lipid panel, fasting plasma glucose, and blood pressure were also undertaken. Finally, data was analyzed by using Statistical Package for Social Sciences version 26.0 for Windows (SPSS Inc., Chicago, IL, USA). All p-values were 2-sided and the level of significance was set at p < 0.05 for all analyses. The frequency of dyslipidemia in this population was disproportionately high (87.4%) with the worst affected subgroup in the 51–60 age band. Further, 98% of the study participants were not aware of their diagnosis. In terms of individual lipid markers, the proportions were as follows: low HDL-C (55.2%); high TC (49.7%); high LDL (44.8%); high TG (38.1%). The mean ± SD, for HDL-C, TC, LDL-C, non-HDL-C, and TG were 45.28 ± 9.60; 205.24 ± 45.77; 130.77 ± 36.15; 160.22 ± 42.09 and 144.5 ± 61.26 mg/dL, respectively. Regarding NCEP ATP III risk criteria, 17.6%, 19.4%, 16.3%, 19.7%, and 54.7% were in high or very high-risk categories for TC, Non-HDL-C, TG, LDL-C, and HDL-C, respectively. Among all respondents, 59.6% had mixed dyslipidemias with TC + TG + LDL-C dominating. In addition, 27.3%, 28.04%, 23.0%, and 8.6% had abnormalities in 1, 2, 3 and 4 lipid abnormalities, respectively. Multivariate logistic regression modeling suggested that dyslipidemia was lower in subjects who were employed (aOR 0.48, 95% CI 0.24–0.97, p = 0.015); self-employed (aOR 0.41, 95% CI 0.17–1.00, p = 0.018); and married (aOR 2.35, 95% CI 1.19–4.66, p = 0.009). A higher likelihood of dyslipidemia was also associated with increasing DBP (aOR 1.04 mmHg (1.00–1.09, p = 0.001) and increasing FPG (aOR 1.02 per 1 mg/dL, 95% CI 1.00–1.05, p = 0.001). Separately, Framingham CVD Risk score estimates suggested that 12.7% and 2.8% were at 10 years CVD high risk or very high-risk strata. High frequency of poor lipid health may be a prominent contributor to the high burden of atherosclerotic CVDs—related mortality and morbidity in Asmara, Eritrea. Consequently, efforts directed at early detection, and evidence-based interventions are warranted. The low awareness rate also points at education within the population as a possible intervention pathway.

technique (computer-based random number generator was applied) within each sub-zone. The appropriate number of households (HH) per EA was subsequently selected using random sampling. Households were excluded from the study if all members of the HH were outside the required age range (≥ 35 to ≤ 85 years). If an abandoned house was encountered during the random selection, it was replaced by the next inhabited household. Eligible participants per HH were selected using the Kish method (a random selection of eligible individuals at the HH level)-eligible members of each household were assigned numbers (starting with the youngest). The Kish grid was then used to identify the participant. In the absence of eligible participants during the visit, a second visit was offered to grant potential participants the opportunity to participate in the survey. In case of the participant's absence during the entire study period, replacement by the current person who was 35 and above was undertaken. All eligible individuals who provided written informed consent were enrolled. Ultimately, we included individuals who met the following criteria: willingness to grant consent, age ≥ 35-85 years, and had permanently resided in Asmara, Eritrea for at least one year. Exclusion criteria included the following: bedridden subjects, pregnancy, serious mental disorder, hearing or intellectual disability, breastfeeding mothers, individuals who were unwilling to provide consent, individuals on specific medications (Steroid, β-adrenergic blockers, thiazide diuretics, anti-HIV medication, statins, among others); and individuals with Diabetes Mellitus (DM). In all, 533 individuals were approached for participation in the study. See Fig. 1. Data collection, measurements, and definitions. Data were collected using a modified version of the WHO NCD STEPS instrument version 3.1 19 . To accommodate unlettered participants, the questionnaire was translated from English to Tigrigna (a local Language) by a language expert. And it was administered to the chosen individuals by trained data collectors. Overall, the instrument incorporates queries on a range of wellestablished cardio-metabolic risk factors and is separated into four sections/Steps. Step 1, includes questions on socio-demographic characteristics (sex, age, the highest level of education, occupation, marital status, ethnicity as well as family history of DM (diabetes mellitus); Step 2 explores lifestyle factors (exercising, sedentary lifestyle, smoking, alcohol consumption, and history of hypertension); Step 3 explores physical measurement (anthropomorphic measurements, blood pressure (BP) measurements, among others); and Step 4 describes biochemical measurements including Fasting plasma glucose (FPG), TG, TC, HDL.
Anthropometry and blood pressure measurement. Anthropometric measurements. Standardized techniques following WHO-STEPS surveillance manual and calibrated equipments were used for anthropometric measurements. All anthropometric measurements were performed by well-trained investigators. Weight, Height, Hip circumference (measured at the widest part of the buttocks), and WC (measured at the iliac crest) were measured as per established protocols using standardized instruments/equipments-a constant tension tape and pre-calibrated digital weighing scale (Sunbeam EB710 digital bathroom scale).
Abdominal obesity was defined as per International Diabetes Federation (IDF) specification (WC ≥ 94 cm in males and ≥ 80 cm in females) 20 . For population-level comparisons, overweight and obesity were defined using body mass index (BMI)-(where BMI = weight in Kilogram (kg)/Height in meters (m) 2 . A per the WHO BMI specification, a BMI < 18.5 kg/m 2 was categorized as underweight; ≥ 18.5-24.9 kg/m 2 was classified as normal 533 individuals were approached for participation in the study 33  www.nature.com/scientificreports/ weight; BMI ≥ 25-29.9 kg/m 2 as being overweight and a BMI ≥ 30 kg/m 2 was classified as obese. The waist-to-hip ratio (WHR) and the waist-to-height ratio (WHtR) were also calculated. For purposes of analysis, a WHR ≥ 0.90 for men and ≥ 0.85 for women were considered abnormal as per IDF guidelines 20 . Dyslipidaemia was defined as any of the following abnormalities: TC ≥ 200 mg/dL (≥ 5.2 mmol/L); LDL-C ≥ 130 mg/dL (≥ 3.4 mmol/L); TG ≥ 150 mg/dL (≥ 1.7 mmol/L); HDL-C (≤ 40 mg/dL (< 1.04 mmol/L) in male and ≤ 50 mg/dL (< 1.3 mmol/L) in female) 9 or reported use of anti-lipid medication. Further, mixeddyslipidemia was defined as the concurrent presence of 2 or more lipoprotein abnormalities.
Data analysis. The completed questionnaires were entered on CSPro software (version 7.0). Keying errors were handled by the double-entry of data. The data was analyzed using Statistical Package for Social Sciences version 20.0 for Windows (SPSS Inc., Chicago, IL, USA). Enrollee characteristics were summarized using frequencies and percentages. Depending on the distribution, continuous data were presented as mean ± standard deviation (SD) or median ± interquartile range (IQR). Data normality, homogeneity of variance, and multicollinearity were tested using suitable statistics. Unadjusted statistical comparisons between categorical variables and categorical outcomes were made using the Chi-square (χ 2 ) test or Fisher exact test. Depending on data distribution, the t-test and one-way analysis of variance (ANOVA) or their non-parametric equivalents (Mann-Whitney U tests or Kruskal Wallis) were employed. Multivariable logistic regression models (backward: conditional) were fitted to identify independent predictors of elevated TC, TG, LDL-C, non-HDL-C, low HDL, and dyslipidemia. Subsequently, crude (COR) and adjusted odds ratios (aOR) and associated 95% confidence (95% CI) were reported. To correct for the impact of multiple comparisons, the Bonferroni correction was applied. All p-values were 2-sided and the level of significance was set at p < 0.05 for all analyses. Missing values or refusals to answer questions were handled by exclusion from analysis. www.nature.com/scientificreports/ Ethical consideration. Administrative and ethical approval was granted by the Eritrean Ministry of Health (EMOH) research proposal review and ethical clearance committee. Written informed consent was obtained from each participant in the local language (Tigrigna) as per the procedures approved by the EMOH ethical committee. Importantly, enrollees were duly informed of their non-negotiable right to instantly terminate their participation in the study. Strict adherence to approved laboratory protocols was observed during specimen collection, processing, and testing. All methods were performed in accordance with the national guidelines and regulations.

Results
Demographic characteristics, patient history, anthropometry, and clinical measurements. 1%) in males. The same pattern was observed across LDL-C and HDL-C risk categories. Mean values for TC, TG, LDL-C, HDL-C, non-HDL-C, and TC/HDL ratio are also presented. See Table 3 for additional information.
Prevalence of mixed dyslipidemias. As seen in Table 4, mixed dyslipidemia, defined as the presence of ≥ 2 lipid abnormalities, was also analyzed. Among all respondents, 59.6% (95% CI 54.6-64.6%) had mixed dyslipidemias. Most notably, respondents with abnormalities in two lipid variables presented either with elevated TG plus low HDL-C (39 (10.2%) or high TC plus high LDL-C, (51 (13.4%). High TC, TG, and LDL-C was the most common presentation (80 (20.9%) in respondents presenting with 3 lipid abnormalities. All four dyslipidemias occurred in 33 (8.6%) of the respondents. See Table 4 for further information.

Logistic regression analysis of factors associated with lipid levels
Factors associated with elevated non-HDL-C, TG, and TC. We summarize here the results of the multivariate models in www.nature.com/scientificreports/   www.nature.com/scientificreports/ Framingham risk scores: magnitude and relationships. Ten years CVD risk scores were estimated using and Framingham CVD Risk Score Calculator. According to these estimates, 43% were at a low risk, 41.5% had a moderate risk; 12.7% had a high risk and 2.8% had a very high-risk of CVD events in the next 10-years. See Fig. 3A. Further, a separate analysis demonstrated that 42.6%, 41%, and 13% of patients with low risk, moderate risk and high risk of CVD events had at least 1 lipid abnormality (dyslipidemia). The dominant abnormalities in individuals in the high-risk category were high LDL-C and TC/HDL-C ratios. See Fig. 3B for additional associations.

Discussion
Although more than 80% of the global burden of CVD is in LMIC, knowledge of important risk factors is largely based on extrapolations from HIC 4 . Furthermore, country-level analysis reveals important intra-country and inter-country differences in the combination of CVD risk factors (age, gender, tobacco smoking, diabetes mellitus (T2DM), lipid abnormalities/dyslipidemia, hypertension, obesity, and a family history of CVDs) 24,25 . For this reason, updated, context-specific data, on the burden or factors associated with CVD incidence, prevalence, morbidity, or mortality has been emphasized 4 . In this study, the first of its kind in Eritrea, we sought to evaluate the prevalence of dyslipidemias and its correlates among adults in Asmara, Eritrea. Moreover, 10-year CVD risk scores were estimated using and Framingham CVD Risk Score Calculator. This study has many remarkable findings. Foremost is the fact that 87.4% of the study respondents had at least 1 lipid abnormality. Methodological differences and heterogeneity in cut-offs for relevant lipid markers notwithstanding; the estimate reported in this study is disproportionately high. However, comparable and, at times, higher estimates have been reported by some investigators in the region. For example, a study conducted in Nigeria reported a prevalence of 85.9% 6 . High values were reported in Lithuania (90%) 26 ; Iran (83.4%) 27 , 85% in Kuwait 28 , South Africa (67.3%) 29 , United Arab Emirates (UAE) (72.5%) 30 , Makelle city, Northern Ethiopia (66.7%) 31 , India (ICMR-INDIAB study) (79%) 32 . A multi-country study (the Africa Middle East Cardiovascular Epidemiological (ACE) Study) reported a high prevalence of 70% 33 . However, a recent systematic and metaanalytical review estimated that the prevalence of dyslipidemia among adults in Africa is-15-50% 4,33 . In other words, the frequency of dyslipidemia in this population is higher than WHO estimates for Africa 34 or what has been reported in some HICs 7 -52% in USA 35 .
Admittedly, the general term "dyslipidemia" can be misleading as it amalgamates disparate lipid components with varying, and at times debatable, contribution to CVD risk 36 . Therefore, the real-world consequences of the high and rising prevalence of dyslipidemia in populations in SSA are poorly understood. Indeed, the lack of prospective, well-controlled randomized trials that addresses the connection between specific CVD and low HDL-C has prevented definite conclusions at this point. Regardless, this study challenges the misconception that dyslipidemia is rare in SSA. Without a doubt, this presumption has created a false sense of security. In much of  38 . In this study, women had significantly higher mean values of TC, HDL-C, and LDL-C. Across, disparate age bands, individuals in the 51-60 age bands had high mean values in TC, LDL-C, Non-HDL-C, and TC/HDL-C. Moreover, high mean values of multiple dyslipidemias were significantly associated with elevated WC, WHR, FPG, and alcohol consumption. While mean values of disparate lipid markers may reflect poorly on the burden of dyslipidemias in a population or a subgroup; the observed congruence between high averages in specific lipids markers and known cardiometabolic risk factors should raise concern. Therefore, the need for comprehensive measures to mitigate the health consequences is evident.
Although the high frequency of low-HDL in populations in SSA is well documented; the importance of this phenomenon is imperfectly understood. Part of the problem is the ambiguity concerning the relationship between HDL-C and CVDs. As previously noted, some investigators have concluded that low HDL-C concentration is not necessarily a marker of cardiometabolic risk in African populations 36,43 . Further, Mendelian randomization studies suggest that HDL-C is a CVD risk marker but not a true causal risk factor 43 . A phenomenon that has complicated this debate is the fact that low HDL-C coexists often with high TG or, like in our study, high LDL-C. In a study in Sweden, 37-38% had hypertriglyceridemia (150 and ≤ 354 mg/dL) with or without low HDL-C 44 . Therefore, dissecting the contribution of individual components (TG or HDL-C) to CVD risk is challenging. Even then, the observation that small, dense more atherogenic LDL-C (sdLDL-C) formation is inversely related to HDL-C concentration should raise concern.
Regarding causality, scholars have attributed the relatively low concentrations of HDL-C in populations across SSA to genetics 45 or a range of modifiable risk factors including insulin resistance/type 2 diabetes mellitus (T2DM), BMI > 25 kg/m 2 , sedentarism/physical inactivity, overconsumption of carbohydrate, infection, and inflammation, among others 46,47 . In our study, we established a positive association between low HDL-C, elevated FPG, and employment status (predominance in the unemployed portion of the population). Similar to Table 6. Association between LDL, TC, TG, HDL, Non-HDL and TG/GDL ratio with key risk factors: results from logistic models. Significant values are in bold. TG triacylglycerol, TC total cholesterol, HDL-C highdensity lipoprotein cholesterol, LDL-C low-density lipoprotein cholesterol. www.nature.com/scientificreports/ otherstudies 46,47 , consumption of alcohol was associated with increased concentration of HDL. In general, the mechanisms underpinning the positive correlation between alcohol consumption and HDL-C concentrations are not known. However, it has been hypothesized that alcohol may increase HDL-C by mediating the transport of apolipoprotein A1 (Apo-A1).
On the basis of currently available data, we believe that a large part of the observed outcome can be explained by physical inactivity and dietary factors. In general, Eritrea's traditional cuisine is starch-rich and emerging evidence supports the notion that sedentarism is a problem in Asmara 18 . Research in runners and the Framingham study demonstrated a strong inverse relationship between HDL-C concentration and physical activity 48 . Likewise, carbohydrate over-nutrition can also lead to enhanced de novo lipogenesis and subsequent induction of ectopic lipid accumulation or aberrant lipid parameters. Unfortunately, individual-level data on nutrition was not documented in this study. Therefore, this explanation, although plausible, is speculative at best. Regardless, much work remains to be done on the relationship between low HDL-C and CVD or mechanisms behind the factors associated with low HDL-C. Addressing the genetics of HDL-C, diet, and exercise, or sedentarism should also be prioritized.
Beyond the debates on the connection between low-HDL-C and CVD; the nexus between TC, LDL-C, Non-HDL-C, and lipid ratios such as TC/HDL and CVD risk is unequivocally 9,33,48 . Genetical, observational, and interventional studies have established a connection between these abnormalities and CVD risk. The wellrespected Multiple Risk Factor Intervention Trial (MRFIT) demonstrated a J-shaped curvilinear relationship between TC and CVD mortality 49 . Further, the evidence that LDL, particularly the smaller, denser more atherogenic (sdLDL) form, prospectively predicts hard CVD events (coronary death, myocardial infarction (MI), and stroke) is unequivocal 9 and decreases generally correlate with improvement in clinical outcomes. Likewise, the INTERHEART Africa investigators concluded that TC/HDL-C and ApoB/apoA1 ratios provide equivalent information about CVD risk 8 . Importantly, abundant data suggest that non-HDL-C concentrations correlate very strongly with apoB and provide comparable clinical information. However, the nexus between TG (either fasting or non-fasting) concentrations and CVD is fraught with uncertainties and controversies 50 . On the whole, the high proportions and, to some extent, mean values of TC, LDL-C, TG, TC/HDL-C, and non-HDL-C observed in this setting should raise concern. Indeed, it's our opinion that the results uncovered in this study may partially account for the high atherosclerotic cardiovascular disease (ASCVD)-related morbidity and general mortality rates observed in Eritrea.
To a large extent, most associations of TG, TC, TC/HDL-C, LDL-C, and Non-HDL-C were in the expected direction. High TC was independently associated with alcohol consumption, hypertension, and increasing FPG. The co-occurrence of high TC in combination with hypertension and elevated FPG is well documented in the region 51 . TC/HDL-C (one of the most potent predictors of CVD risk) exhibited an independent association with age, being married, elevated WC, presence of hypertension, and elevated FPG. The association between www.nature.com/scientificreports/ TC/HDL-C and known CVD risk markers appears to suggest that it can be a good marker of CVD risk in this population. Similar to other studies 49 , elevated LDL-C was associated with alcohol consumption, WC, and hypertension. These risk factors were also associated with non-HDL-C and TG (add sex) in this study. Remarkably, a large proportion of participants with elevated LDL-C were in the high-risk category in the 10-year Framingham CVD Risk estimates. Another interesting relationship was the observed association between TC/HDL-C ≥ 5 ratio and sex (higher in males); WC, hypertension, and FPG. Despite the broad agreement between this study and other studies, notable exceptions were observed. For example, BMI ≥ 25 kg/m 2 had only one association (LDL-C). This was in contrast to studies that have uncovered a significant relationship between elevated BMI, high TG, and low HDL-C 52 . We are unable to provide definitive explanations why BMI is a poor marker of dyslipidemia in this setting. However, this unexpected finding highlights the fact that the frequency of dyslipidemia can be high even in populations with relatively low prevalence of general obesity. Interestingly, we found a significant relationship between WC and multiple dyslipidemias in the multivariate analysis-Non-HDL-C, LDL-C, TC/HDL, and TG. As previously noted 53 , the use of WC for public health screening or clinical evaluation of patients is still limited in Eritrea. In this regard, the current study merely adds to the evidence of its utility and relevance in Eritrea.
Further, troubling associations and patterns were apparent in this population. The high number of women in NCEP ATP III high risk of very high-risk category; the higher likelihood of dyslipidemia in the unemployed; large number of individuals who are divorced/or widowed or without formal education in the high-risk category in Framingham 10-year general CVD risk estimate. The clustering of CVD risk markers among the unemployed, in populations of low socioeconomic status, or among the less educated strata of the society is well documented 46 . According to some authors, education mediates the risk of CVD through urbanization, unemployment, access to information/awareness, food, social support and cohesion, and individual health behaviors. The influence of these factors on CVD risk is poorly documented in populations across Eritrea.
By most accounts, mixed dyslipidaemia is both poorly described and inadequately addressed in current guidelines 9 . In this study, mixed dyslipidemia was relatively common (68.6%). For example, 28.04%, 23%, and 8.6% of the study participants had abnormalities in two, three, and four lipid components, respectively. The most common combination was high TC + TG + LDL-C (20.9%).

Proportion of Participants (%)
Low Risk Moderate Risk High Risk www.nature.com/scientificreports/ HDL-C and high TG + low HDL-C was also substantial-13.4% versus 10.2%, respectively. In general, the figures in our study are higher than those from Canada, Iran, and France 25,27,54 . This aside, we have to emphasize the fact that much of what we know about mixed dyslipidemia is based on studies from HIC. Therefore, rigorous prospective investigations are necessary to determine the risk associated with the simultaneous coexistence of specific lipid abnormalities in SSA. Nevertheless, there is little doubt that these combinations, by themselves, can accentuate CVD risk 25 . For example, epidemiologic studies suggest that the co-occurrence of high TG + low HDL-C concentrations (atherogenic dyslipidemia) is a strong risk factor for coronary heart disease (CHD) with post hoc analyses of several studies suggesting that these individuals have the highest rate of hard coronary events 53,55 . Strengths and limitations. To our knowledge, this is the first population-based study on the prevalence of elevated concentrations of TC, LDL-C, non-HDL-C, and TG and low HDL-C concentrations in adults in Eritrea. Regardless, this study is not without limitations. First, the cross-sectional nature of the study limits the dissection of causality. In addition, the fact that the population was mostly composed of urban residents limits the generalizability of our findings. The use of a researcher-administered questionnaire to capture data on specific variables may be affected by the recall, social desirability, and outcome misclassification biases. Lastly, the Framingham risk score and Friedewald equation for LDL-C estimation have not been validated in this population; hence the results should be used with caution. Despite the above limitations, and in the absence of longitudinal studies, this investigation represents a major first step towards getting baseline data on lipid profiles in Asmara, Eritrea. The assessment of all major lipoproteins and proportions of mixed dyslipidemias is rare in community-based studies in SSA and adds another layer to the information this paper provides on lipid abnormalities and CVD risk. Finally, the attempt to analyze 10-year general CVD risk adds to its uniqueness.

Conclusion
This study uncovered many important findings.First, the prevalence of dyslipidemia is high in the general adult population in Asmara Eritrea. In reducing the frequency, the dominant abnormalities were: low HDL-C (55.2%), high TC (49.7%); high LDL (44.8%), and high TG. Analysis based on NCEP ATP III specifications demonstrated that women were disproportionately affected across all TC risk strata: borderline (71 (57.3%) in female vs 53 (42.7) in males) and high risk (52 (76.5%) females vs 16 (23.5%) in males). A similar pattern was observed across non-HDL-C, LDL-C, and HDL-C risk bands. Further, 59.6% had mixed with TC + TG + LDL-C combination predominating. Interestingly, multivariate logistic regression demonstrated that the presence of dyslipidemia was lower in individuals who were employed or self-employed; higher in those who were married; and was positively correlated with increasing DBP and increasing FPG. In terms of 10-year Framingham risk scores, 166 (43%), 160 (41.5%), 49 (12.7%), 11 (2.8%) were in the low-risk, moderate risk, high-risk, and very high-risk strata. As a whole, these unique data strongly suggest that dyslipidemia may be a principal contributor to CVD risk in this setting. The level of awareness is low and most study participants were not receiving lipid-lowering therapy as specified in international guidelines. Significantly, these observations call for concerted, effort directed at scaling up early recognition and treatment, including optimal pharmacological and non-pharmacological therapy at all levels of care. Lastly, further research is needed to corroborate our findings and to determine the ethnic-specific relationship between specific lipid markers or mixed dyslipidemias and CVD risk in this population.

Data availability
The dataset supporting the conclusions of this article are available from the corresponding author on reasonable request. www.nature.com/scientificreports/